'''simple docstring'''

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
  from transformers import TvltImageProcessor

if is_speech_available():
  from transformers import TvltFeatureExtractor

from transformers import TvltProcessor


@require_torch
class 					_lowercase						(   unittest.TestCase ):
      '''simple docstring'''


      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	'''ZinengTang/tvlt-base'''
         UpperCAmelCase__				:					int 					=	tempfile.mkdtemp()




      def 		lowerCAmelCase__       (					self       ,**lowerCamelCase_				)      ->      Union[str, Any]:



         '''simple docstring'''



         return TvltImageProcessor.from_pretrained(self.checkpoint       ,**__snake_case				)




      def 		lowerCAmelCase__       (					self       ,**lowerCamelCase_				)      ->      Optional[Any]:



         '''simple docstring'''



         return TvltFeatureExtractor.from_pretrained(self.checkpoint       ,**__snake_case				)




      def 		lowerCAmelCase__       (					self				)      ->      List[Any]:



         '''simple docstring'''



         shutil.rmtree(self.tmpdirname				)




      def 		lowerCAmelCase__       (					self				)      ->      str:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.get_image_processor()
         UpperCAmelCase__				:					Optional[Any] 					=	self.get_feature_extractor()

         UpperCAmelCase__				:					List[str] 					=	TvltProcessor(image_processor=__snake_case       ,feature_extractor=__snake_case				)
         processor.save_pretrained(self.tmpdirname				)
         UpperCAmelCase__				:					List[str] 					=	TvltProcessor.from_pretrained(self.tmpdirname				)

         self.assertIsInstance(processor.feature_extractor       ,__snake_case				)
         self.assertIsInstance(processor.image_processor       ,__snake_case				)




      def 		lowerCAmelCase__       (					self				)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.get_image_processor()
         UpperCAmelCase__				:					Any 					=	self.get_feature_extractor()

         UpperCAmelCase__				:					Any 					=	TvltProcessor(image_processor=__snake_case       ,feature_extractor=__snake_case				)

         UpperCAmelCase__				:					List[str] 					=	np.ones([12000]				)

         UpperCAmelCase__				:					List[str] 					=	feature_extractor(__snake_case       ,return_tensors='''np'''				)
         UpperCAmelCase__				:					Tuple 					=	processor(audio=__snake_case       ,return_tensors='''np'''				)

         for key in audio_dict.keys():
            self.assertAlmostEqual(audio_dict[key].sum()       ,input_processor[key].sum()       ,delta=1e-2				)




      def 		lowerCAmelCase__       (					self				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	self.get_image_processor()
         UpperCAmelCase__				:					int 					=	self.get_feature_extractor()

         UpperCAmelCase__				:					Any 					=	TvltProcessor(image_processor=__snake_case       ,feature_extractor=__snake_case				)

         UpperCAmelCase__				:					Dict 					=	np.ones([3, 224, 224]				)

         UpperCAmelCase__				:					Dict 					=	image_processor(__snake_case       ,return_tensors='''np'''				)
         UpperCAmelCase__				:					Dict 					=	processor(images=__snake_case       ,return_tensors='''np'''				)

         for key in image_dict.keys():
            self.assertAlmostEqual(image_dict[key].sum()       ,input_processor[key].sum()       ,delta=1e-2				)




      def 		lowerCAmelCase__       (					self				)      ->      Any:



         '''simple docstring'''



         UpperCAmelCase__				:					int 					=	self.get_image_processor()
         UpperCAmelCase__				:					List[str] 					=	self.get_feature_extractor()

         UpperCAmelCase__				:					Optional[int] 					=	TvltProcessor(image_processor=__snake_case       ,feature_extractor=__snake_case				)

         UpperCAmelCase__				:					Tuple 					=	np.ones([12000]				)
         UpperCAmelCase__				:					List[Any] 					=	np.ones([3, 224, 224]				)

         UpperCAmelCase__				:					str 					=	processor(audio=__snake_case       ,images=__snake_case				)

         self.assertListEqual(list(inputs.keys()				)       ,['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']				)

         # test if it raises when no input is passed
         with pytest.raises(__snake_case				):
            processor()




      def 		lowerCAmelCase__       (					self				)      ->      List[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Any 					=	self.get_image_processor()
         UpperCAmelCase__				:					Dict 					=	self.get_feature_extractor()

         UpperCAmelCase__				:					Dict 					=	TvltProcessor(image_processor=__snake_case       ,feature_extractor=__snake_case				)

         self.assertListEqual(
             processor.model_input_names       ,image_processor.model_input_names + feature_extractor.model_input_names       ,msg='''`processor` and `image_processor`+`feature_extractor` model input names do not match'''       ,)








import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

_SCREAMING_SNAKE_CASE     :					int      =       logging.getLogger()
def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							argparse.ArgumentParser()
     parser.add_argument('''-f'''							)
     snake_case											=							parser.parse_args()
     return args.f

class     A__   (       snake_case__      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							logging.StreamHandler(sys.stdout       )
         logger.addHandler(__snake_case       )
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							get_gpu_count()

         if n_gpu > 1:
              pass
              # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
              # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
              # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
              # cmd = [sys.executable] + distributed_args + args
              # execute_subprocess_async(cmd, env=self.get_env())
              # XXX: test the results - need to save them first into .json file
         else:
              args.insert(0     ,   '''run_glue_deebert.py'''       )
              with patch.object(__snake_case     ,   '''argv'''     ,   __snake_case       ):
                   snake_case											=							run_glue_deebert.main()
                   for value in result.values():
                        self.assertGreaterEqual(__snake_case     ,   0.666       )




    @slow
    @require_torch_non_multi_gpu
    def 						a_     (			self       ):
         snake_case											=							'''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
         self.run_and_check(__snake_case       )

         snake_case											=							'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(__snake_case       )

         snake_case											=							'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(__snake_case       )




